Pharmacogenomics (PGx) is the hope for the full optimization of drug therapy while minimizing the accompanying adverse drug events that cost billions of dollars annually. Since years before the century, it has been known that inter-individual variations contribute to differences in specific drug responses. It is the bridge to what is well-known today as “personalized medicine”. Addressing the drug’s pharmacokinetics and pharmacodynamics is one of the features of this science, owing to patient characteristics that vary on so many occasions. Mainly in the liver parenchymal cells, intricate interactions between the drug molecules and enzymes family of so-called “Cytochrome P450” occur which hugely affects how the body will react to the drug in terms of metabolism, efficacy, and safety. Single nucleotide polymorphisms, once validated for a transparent and credible clinical utility, can be used to guide and ensure the succession of the pharmacotherapy plan. Novel tools of pharmacoeconomics science are utilized extensively to assess cost-effective pharmacogenes preceding the translation to the bedside. Drug development and discovery incorporate a drug-gene perspective and save more resources. Regulations and laws shaping the clinical PGx practice can be misconceived; however, these pre-/post approval processes ensure the product’s safety and efficacy. National and international regulatory agencies seek guidance on maintaining conduct in PGx practice. In this patient-centric era, social and legal considerations manifest in a way that makes them unavoidable, involving patients and other stakeholders in a deliberate journey toward utmost patient well-being. In this comprehensive review, we contemporarily addressed the scientific leaps in PGx, along with various challenges that face the proper implementation of personalized medicine in Egypt. These informative insights were drawn to serve what the Egyptian population, in particular, would benefit from in terms of knowledge and know-how while maintaining the latest global trends. Moreover, this review is the first to discuss various modalities and challenges faced in Egypt regarding PGx, which we believe could be used as a pilot piece of literature for future studies locally, regionally, and internationally.

Accurate species-level classification of prokaryotic 16S rRNA sequences remains difficult: existing tools rely on exact alignment, k-mer heuristics, or phylogenetic placement and are limited by incomplete reference databases. Deep learning approaches in microbial genomics have focused largely on whole-genome metagenomics, leaving 16S taxonomy under-supported. We present DeepTaxa, a hybrid CNN-BERT framework that pairs a multiscale CNN with a transformer trained from scratch on the DNABERT-2 BPE vocabulary, producing parallel rank-specific predictions across the seven Linnean ranks. On the Greengenes2 2024.09 test set, DeepTaxa achieves species-level accuracy of 92.96% and F1 of 0.9212 (3-seed mean; cross-seed standard deviation ≤0.0008 F1 at every rank), with F1 above 0.99 from domain through class and a species-level expected calibration error of 0.0242. DeepTaxa exceeds DADA2 (90.05%) and QIIME 2 (85.01%) at the species rank on the same held-out test set, with larger gains over the k-mer-based classifiers SINTAX and Kraken 2. Performance degrades smoothly with decreasing training-set similarity (species F1 from 0.95 to 0.45), and a dedicated V3–V4 amplicon checkpoint reaches 87.55% species accuracy from an approximately 420 bp window. Source code, trained checkpoints for full-length 16S and V3–V4 amplicons, curated datasets, and reproducible workflows are publicly available at github.com/systems-genomics-lab/deeptaxa and huggingface.co/systems-genomics-lab/deeptaxa.